检索结果-内蒙古大学图书馆

17th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2022

作者： Huang, Feihong Wang, Da-Han Ye, Hai-Li Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9789811950957

Weakly-supervised semantic segmentation (WSSS) receives increasing attentions from the community in recent years as it leverages the weakly annotated data to solve the problem of lacking of fully annotated data. Among them, the WSSS method based on image-level annotation is the most direct and effective while the image-level annotation is easy to obtain. Most advanced methods use class activation maps (CAM) as initial pseudo-labels, however, they only identify local regions of the target, while ignoring the context information among local regions. To solve this problem, this paper proposes a deformable convolution based self-attention module (DSAM), which introduces a pixel relationship matrix, to learn the contextual information of the image. A regularization loss is introduced to narrow the distance between the DSAM and the CAM. Compared to the base CAM method, our method can identify more target features and robustly improve the performance of WSSS without training the classifier multiple times. Our proposed method achieves the mIoU of 65.5% and 66.8% on the Pascal VOC 2012 val and test sets, respectively, demonstrating the feasibility of the method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Content and structure based attention for graph node classification

引用

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8329-8343页

作者： Chen, Yong Xie, Xiao-Zhu Weng, Wei College of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

Graph-structured data is ubiquitous in real-world applications, such as social networks, citation networks, and communication networks. Graph neural network (GNN) is the key to process them. In recent years, graph attention networks (GATs) have been proposed for node classification and achieved encouraging performance. It focuses on the content associated on nodes to evaluate the attention weights, and the rich structure information in the graph is almost ignored. Therefore, we propose a multi-head attention mechanism to fully employ node content and graph structure information. The core idea is to introduce the interactions in the topological structure into the existing GATs. This method can more accurately estimate the attention weights among nodes, thereby improving the convergence of GATs. Second, the mechanism is lightweight and efficient, requires no training to learn, can accurately analyze higher-order structural information, and can be strongly interpreted through heatmaps. We name the proposed model content- and structure-based graph attention network (CSGAT). Furthermore, our proposed model achieves state-of-the-art performance on a number of datasets in node classification. The code and data are available at https://***/CroakerShark/CSGAT. © 2024 - IOS Press. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Exploring Joint Information of Multi-Scales for Vehicle Re-Identification 22

Exploring Joint Information of Multi-Scales for Vehicle Re-I...

引用

8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Zhou, Yongjie Wang, Dahan School of Computer and Information Engineering Xiamen University of Technology Xiamen Chinese Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Vehicle re-identification (re-ID) is an essential component of intelligent video surveillance, which attempts to solve the problem of retrieving specific vehicle instances. The technical challenge is mainly the requirement that the algorithm be robust under different viewing angles, resolutions, occlusions, and lighting conditions, yet different conditions can arise where the appearance of the exact vehicle varies dramatically. In contrast, the appearance of two different vehicles is exceptionally similar. To overcome this enormous challenge: we adopt a novel multi-scales attention network architecture, which will feature representation for feature maps at different scales to enhance the recognition of intricate parts, and introduce an inter-coordinate attention module to build interdependencies between component positions, making it possible to build a more robust vehicle model feature representation, and finally this paper also proposes a camera-based vehicle re localization method that enables the model to calculate the vehicle occurrence probability using the potential information among road network cameras as a post-processing step to re-correct the confidence level of the re-identification ranking. Experimental results show that the method outperforms the reference method in all indexes even without post-processing. At the same time, the camera confidence correction can be used in overlay with the reordering clustering correction method, which can improve the results by four percentage points and MAP up to 81.4%. © 2022 ACM.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Dynamic Updating of Partial Feature Maps for Image-based Real-Time recognition of Online Handwritten Chinese Text 22

Dynamic Updating of Partial Feature Maps for Image-based Rea...

引用

8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Wu, Yuefeng Zhong, Ying Wang, Da-Han Chi, Xueke School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Online handwritten Chinese text recognition (OHCTR) has made certain progress in recent years. Most methods currently used are image-based text recognition which has achieved some success. However, it should be noticed that, it is difficult to smoothly recognize online handwritten texts in real-time, especially for long text lines. For real-time recognition of OHCT, the feature extraction of ever-extending text lines will be time-consuming and make the system un-humanization. Moreover, the feature extraction of the non-written part will be performed repeatedly, which is not suitable and has not been addressed by previous works. In this paper, we propose a method to dynamically update partial feature maps of the newly written part for real-time OHCTR. The basic idea is to update the feature maps according to the newly written stroke, which can avoid repeated convolutional computations and thus improve the recognition speed. The proposed method can handle the case of delayed strokes, and can greatly improve the speed of real-time recognition without any loss of accuracy. Experiments on the CASIA-OLHWDB and the ICDAR-2013 competition database demonstrate that our real-time recognition speed are multiplier increased compared with traditional text line recognition methods. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Attention-based Deep Learning Methods for Document Layout Analysis 22

Attention-based Deep Learning Methods for Document Layout An...

引用

8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Sun, Hao-Yue Zhong, Ying Wang, Da-Han School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Deep learning has unique advantages in document layout analysis. In this paper, we propose to use the attention mechanism to improve the deep learning based methods for document layout analysis, and conducted comprehensive experiments to evaluate the effects of different models/modules/backbones. Firstly, the evaluated backbones include ResNet-50 and ResNet-101 for their high performance, and the popularly used Fast RCNN and Faster RCNN based methods. Secondly, for the loss function, the Focal loss function is also adopted to improve the performance of document layout analysis. Most importantly, we propose to integrate the attention mechanism into the deep learning-based methods to improve the performance. Specifically, when calculating the residual using this ResNet network, we use the attention to perform a dot product operation on the degraded accuracy to correct the residual error of the network calculation, which obtains a higher detection accuracy. We conducted experiments on the PubLayNet, DocBank and ICDAR-2013 datasets for evaluation. Experimental results show that, with the attention mechanism, the basic deep learning-based methods has been significantly improved in the task of document layout analysis, demonstrating the effectiveness of the proposed method. The focal loss that is designed to overcome the problem of imbalance number of training samples is also effective for document layout analysis. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

CDE-Net: A semi-supervised nucleus instance segmentation method based on center deviation estimation 22

CDE-Net: A semi-supervised nucleus instance segmentation met...

引用

8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Yan, Yi Wang, Da-Han Ye, Haili Zhu, Shunzhi Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Cell nucleus segmentation is an important step in the diagnosis of tumor malignancy. In the case of out-of-focus, the cell nucleus boundary tends to be blurred, which increases the difficulty of cell segmentation and marking. Therefore, in the case of marking only some core instances, it is very important to realize the segmentation of core instances. In this paper, we propose a semi-supervised nucleus instance segmentation method based on anchor bias prediction that is achieved by our proposed center deviation estimation network (CDE-Net). By introducing the CDE branch to predict the offset degree of the anchor frame, and weighting the confidence of the anchor frame, more accurate prediction results can be obtained. The model adopts the semi-supervised training method to progressively learn the CDE-Net model, which alleviates the problem of imbalance between positive and negative samples caused by only marking part of the nuclear samples. The experimental results show that CDE-Net is superior to Mask R-CNN in many evaluation indicators, and achieves SOTA on the partially labelled MoNuSeg data set. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Image Segmentation Based on Grouped Convolution 6

Brain Tumor Image Segmentation Based on Grouped Convolution

引用

2022 6th International Conference on Machine vision and Information Technology, CMVIT 2022

作者： Wu, Mingliang Ye, Hai-Li Wu, Yun Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361000 China

The automatic segmentation of MRI multi-modal images of brain tumors is one of the important research contents of disease detection and analysis. Due to the heterogeneity of tumors, it is difficult to achieve efficient and accurate automatic segmentation of brain tumors. Traditional segmentation methods based on machine learning cannot handle complex scenes such as complex edges and overlapping categories. In clinical assisted diagnosis, it is of great significance to apply deep learning to two-dimensional natural image segmentation and three-dimensional medical image segmentation. In this paper, we propose a three-dimensional network model to achieve precise segmentation of brain tumors. The model adopts an encoder-decoder structure and replaces ordinary convolution with grouped convolution to reduce network parameters and improve network performance. The model improves the problem of information exchange between different groups through channel mixing. Experiments conducted on the BraTS (Brain Tumor Segmentation) 2018 challenge dataset prove that our network greatly reduces the computational cost while ensuring segmentation accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Image Segmentation of Endoscopic Cataract Based on Semantic Segmentation 6

Image Segmentation of Endoscopic Cataract Based on Semantic ...

引用

2022 6th International Conference on Machine vision and Information Technology, CMVIT 2022

作者： Chen, Jingshi Ye, Haili Yin, Huayi Wang, Da-Han Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361000 China

Medical imaging technology has become one of the indispensable computer-assisted intervention methods in clinical disease diagnosis and treatment, including identifying and locating lesion areas, detecting and segmenting tissue and organ lesions in different modalities. The wide application of medical image analysis in clinical examination and medical auxiliary diagnosis has effectively improved the diagnostic efficiency of doctors. In this paper, we propose a novel and effective model to achieve semantic segmentation of cataract datasets. This model uses the self-supervised method BYOL for parameter pre-training, which improves the model's ability to extract image consistency features. In addition, we have added a lightweight Coordinate attention mechanism to the backbone network to enable the model to independently learn the correlation between the channel and the space and enhance the ability of network feature expression. Experiments are conducted on the cataract endoscopy fine-grained segmentation data set, showing the effectiveness of the proposed method for segment the organs and surgical instruments in the cataract surgical microscope image, which demonstrates the accuracy and robustness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

ResAsapp: An Effective Convolution to Distinguish Adjacent Pixels For Scene Text Detection 11

ResAsapp: An Effective Convolution to Distinguish Adjacent P...

引用

11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Weng, Kangming Du, Xia Chen, Kunze Wang, Dahan Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450397056

The segmentation-based approach is an essential direction of scene text detection, and it can detect arbitrary or curved text, which has attracted the increasing attention of many researchers. However, extensive research has shown that the segmentation-based method will be disturbed by adjoining pixels and cannot effectively identify the text boundaries. To tackle this problem, we proposed a ResAsapp Conv based on the PSE algorithm. This convolution structure can provide different scale visual fields about the object and make it effectively recognize the boundary of texts. The method's effectiveness is validated on three benchmark datasets, CTW1500, Total-Text, and ICDAR2015 datasets. In particular, on the CTW1500 dataset, a dataset full of long curve text in all kinds of scenes, which is hard to distinguish, our network achieves an F-measure of 81.2%. © 2022 ACM.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Twofold Siamese Network with Attentional Feature Fusion for Object Tracking 22

Twofold Siamese Network with Attentional Feature Fusion for ...

引用

8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Huang, Huang Chen, Si Wang, Da-Han Xu, Huarong School of Computer and Information Engineering Xiamen University of Technology China Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450396110

Object tracking is still a critical and challenging problem in computer vision. More and more researchers pay attention to applying deep learning to obtain the powerful feature for robust tracking. Nowadays, feature fusion is an essential part of Siamese tracking architectures. However, the existing feature fusion methods usually provide a fixed linear aggregation of feature maps, and this combination may not be appropriate for a specific object. In this paper, a twofold Siamese network, named SD-Siam, is proposed to extract the features of the object effectively. The template branch and the search branch are both composed of a deep layer sub-network and a shallow layer sub-network, which is used for feature fusion of the different network layers. Moreover, an attentional feature fusion scheme is employed to better fuse scale-inconsistent features, where a multi-scale channel attention module is used to fuse different scales of features. In addition, we respectively evaluate similarity measures for the features of deep layer sub-networks and the fused features of the template branch and the search branch, and then these two similarity response maps are added to obtain the tracking result. Experiments show the proposed SD-Siam outperforms representative trackers on several challenging benchmarks. © 2022 ACM.

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：